{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TimeEval parameter optimization result analysis (Part 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import json\n",
    "import warnings\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pathlib import Path\n",
    "from timeeval import Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define data and results folder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available result directories:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/projects/akita/results/2021-09-27_shared-optim'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-06_optim-part1'),\n",
       " PosixPath('/home/projects/akita/results/2021-09-27_default-params-1&2&3&4-merged'),\n",
       " PosixPath('/home/projects/akita/results/.ipynb_checkpoints')]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Selecting:\n",
      "Data path: /home/projects/akita/data/test-cases\n",
      "Result path: /home/projects/akita/results/2021-10-06_optim-part1\n"
     ]
    }
   ],
   "source": [
    "# constants and configuration\n",
    "data_path = Path(\"/home/projects/akita/data\") / \"test-cases\"\n",
    "result_root_path = Path(\"/home/projects/akita/results\")\n",
    "experiment_result_folder = \"2021-10-06_optim-part1\"\n",
    "\n",
    "# build paths\n",
    "result_paths = [d for d in result_root_path.iterdir() if d.is_dir()]\n",
    "print(\"Available result directories:\")\n",
    "display(result_paths)\n",
    "\n",
    "result_path = result_root_path / experiment_result_folder\n",
    "print(\"\\nSelecting:\")\n",
    "print(f\"Data path: {data_path.resolve()}\")\n",
    "print(f\"Result path: {result_path.resolve()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load results and dataset metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading results from /home/projects/akita/results/2021-10-06_optim-part1\n"
     ]
    }
   ],
   "source": [
    "# load results\n",
    "print(f\"Reading results from {result_path.resolve()}\")\n",
    "df = pd.read_csv(result_path / \"results.csv\")\n",
    "\n",
    "# add dataset_name column\n",
    "df[\"dataset_name\"] = df[\"dataset\"].str.split(\".\").str[0]\n",
    "\n",
    "# load dataset metadata\n",
    "dmgr = Datasets(data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract target optimized parameter names that were iterated in this run (per algorithm):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DBStream ['radius', 'min_weight', 'alpha', 'lambda', 'shared_density']\n",
      "DeepAnT []\n",
      "Donut ['latent_size', 'linear_hidden_size']\n",
      "DWT-MLEAD ['quantile_epsilon']\n",
      "FFT ['fft_parameters', 'local_outlier_threshold', 'max_sign_change_distance']\n"
     ]
    }
   ],
   "source": [
    "algo_param_mapping = {}\n",
    "algorithms = df[\"algorithm\"].unique()\n",
    "param_ignore_list = [\"max_anomaly_window_size\", \"window_size\"]\n",
    "\n",
    "for algo in algorithms:\n",
    "    param_sets = df.loc[df[\"algorithm\"] == algo, \"hyper_params\"].unique()\n",
    "    param_sets = [json.loads(ps) for ps in param_sets]\n",
    "    param_names = np.unique([name for ps in param_sets for name in ps if name not in param_ignore_list])\n",
    "    search_space = set()\n",
    "    for param_name in param_names:\n",
    "        values = []\n",
    "        for ps in param_sets:\n",
    "            try:\n",
    "                values.append(ps[param_name])\n",
    "            except:\n",
    "                pass\n",
    "        values = np.unique(values)\n",
    "        if values.shape[0] > 1:\n",
    "            search_space.add(param_name)\n",
    "    algo_param_mapping[algo] = list(search_space)\n",
    "\n",
    "for algo in algo_param_mapping:\n",
    "    print(algo, algo_param_mapping[algo])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract optimized parameters and their values (columns: optim_param_name and optim_param_value) for each experiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_hyper_params(algo):\n",
    "    param_names = algo_param_mapping[algo]\n",
    "    def extract(value):\n",
    "        params = json.loads(value)\n",
    "        result = None\n",
    "        for name in param_names:\n",
    "            try:\n",
    "                value = params[name]\n",
    "                result = pd.Series([name, value], index=[\"optim_param_name\", \"optim_param_value\"])\n",
    "                break\n",
    "            except KeyError:\n",
    "                pass\n",
    "        if result is None:\n",
    "            return pd.Series([np.nan, np.nan], index=[\"optim_param_name\", \"optim_param_value\"])\n",
    "        return result\n",
    "    return extract\n",
    "\n",
    "df[[\"optim_param_name\", \"optim_param_value\"]] = \"\"\n",
    "for algo in algo_param_mapping:\n",
    "    df_algo = df.loc[df[\"algorithm\"] == algo]\n",
    "    df.loc[df_algo.index, [\"optim_param_name\", \"optim_param_value\"]] = df_algo[\"hyper_params\"].apply(extract_hyper_params(algo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract window size parameters (dependent params) and convert them into multiples of the dataset period size:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dependent_param_names = [\"neighbourhood_size\", \"window_size\"]\n",
    "\n",
    "def extract_window_param(value, param_name=\"\"):\n",
    "    params = json.loads(value)\n",
    "    try:\n",
    "        return params[param_name]\n",
    "    except KeyError:\n",
    "        return 0\n",
    "\n",
    "for param_name in dependent_param_names:\n",
    "    s_windows = df[\"hyper_params\"].apply(extract_window_param, param_name=param_name)\n",
    "    df2 = df[s_windows > 0][[\"dataset\"]].copy()\n",
    "    df2[param_name] = s_windows[df2.index]\n",
    "    df2[\"period_size\"] = df2[\"dataset\"].apply(lambda d: dmgr.get((\"GutenTAG\", d)).period_size)\n",
    "    df2[\"optim_param_name\"] = param_name\n",
    "    df2[\"optim_param_value\"] = df2[param_name] / df2[\"period_size\"]\n",
    "    df2[\"optim_param_value\"] = (df2[\"optim_param_value\"]\n",
    "                                .fillna(df2[param_name])\n",
    "                                .round(1)\n",
    "                                .replace(50., 0.5)\n",
    "                                .replace(100, 1.0)\n",
    "                                .replace(150, 1.5)\n",
    "                                .replace(200, 2.0))\n",
    "    df.loc[df2.index, [\"optim_param_name\", \"optim_param_value\"]] = df2[[\"optim_param_name\", \"optim_param_value\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_scores_df(algorithm_name, dataset_id, optim_params, repetition=1):\n",
    "    params_id = df.loc[(df[\"algorithm\"] == algorithm_name) & (df[\"collection\"] == dataset_id[0]) & (df[\"dataset\"] == dataset_id[1]) & (df[\"optim_param_name\"] == optim_params[0]) & (df[\"optim_param_value\"] == optim_params[1]), \"hyper_params_id\"].item()\n",
    "    path = (\n",
    "        result_path /\n",
    "        algorithm_name /\n",
    "        params_id /\n",
    "        dataset_id[0] /\n",
    "        dataset_id[1] /\n",
    "        str(repetition) /\n",
    "        \"anomaly_scores.ts\"\n",
    "    )\n",
    "    return pd.read_csv(path, header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define plotting functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_use_plotly = True\n",
    "try:\n",
    "    import plotly.offline\n",
    "except ImportError:\n",
    "    default_use_plotly = False\n",
    "\n",
    "def plot_scores(algorithm_name, dataset_name, use_plotly: bool = default_use_plotly, **kwargs):\n",
    "    if isinstance(algorithm_name, tuple):\n",
    "        algorithms = [algorithm_name]\n",
    "    elif not isinstance(algorithm_name, list):\n",
    "        raise ValueError(\"Please supply a tuple (algorithm_name, optim_param_name, optim_param_value) or a list thereof as first argument!\")\n",
    "    else:\n",
    "        algorithms = algorithm_name\n",
    "    # construct dataset ID\n",
    "    dataset_id = (\"GutenTAG\", f\"{dataset_name}.unsupervised\")\n",
    "\n",
    "    # load dataset details\n",
    "    df_dataset = dmgr.get_dataset_df(dataset_id)\n",
    "\n",
    "    # check if dataset is multivariate\n",
    "    dataset_dim = df.loc[df[\"dataset_name\"] == dataset_name, \"dataset_input_dimensionality\"].unique().item()\n",
    "    dataset_dim = dataset_dim.lower()\n",
    "    \n",
    "    auroc = {}\n",
    "    df_scores = pd.DataFrame(index=df_dataset.index)\n",
    "    skip_algos = []\n",
    "    algos = []\n",
    "    for algo, optim_param_name, optim_param_value in algorithms:\n",
    "        optim_params = f\"{optim_param_name}={optim_param_value}\"\n",
    "        algos.append((algo, optim_params))\n",
    "        # get algorithm metric results\n",
    "        try:\n",
    "            auroc[(algo, optim_params)] = df.loc[\n",
    "                (df[\"algorithm\"] == algo) & (df[\"dataset_name\"] == dataset_name) & (df[\"optim_param_name\"] == optim_param_name) & (df[\"optim_param_value\"] == optim_param_value),\n",
    "                \"ROC_AUC\"\n",
    "            ].item()\n",
    "        except ValueError:\n",
    "            warnings.warn(f\"No ROC_AUC score found! Probably {algo} with params {optim_params} was not executed on {dataset_name}.\")\n",
    "            auroc[(algo, optim_params)] = -1\n",
    "            skip_algos.append((algo, optim_params))\n",
    "            continue\n",
    "\n",
    "        # load scores\n",
    "        training_type = df.loc[df[\"algorithm\"] == algo, \"algo_training_type\"].values[0].lower().replace(\"_\", \"-\")\n",
    "        try:\n",
    "            df_scores[(algo, optim_params)] = load_scores_df(algo, (\"GutenTAG\", f\"{dataset_name}.{training_type}\"), (optim_param_name, optim_param_value)).iloc[:, 0]\n",
    "        except (ValueError, FileNotFoundError):\n",
    "            warnings.warn(f\"No anomaly scores found! Probably {algo} was not executed on {dataset_name} with params {optim_params}.\")\n",
    "            df_scores[(algo, optim_params)] = np.nan\n",
    "            skip_algos.append((algo, optim_params))\n",
    "    algorithms = [a for a in algos if a not in skip_algos]\n",
    "\n",
    "    if use_plotly:\n",
    "        return plot_scores_plotly(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs)\n",
    "    else:\n",
    "        return plot_scores_plt(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs)\n",
    "\n",
    "def plot_scores_plotly(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs):\n",
    "    import plotly.offline as py\n",
    "    import plotly.graph_objects as go\n",
    "    import plotly.figure_factory as ff\n",
    "    import plotly.express as px\n",
    "    from plotly.subplots import make_subplots\n",
    "\n",
    "    # Create plot\n",
    "    fig = make_subplots(2, 1)\n",
    "    if dataset_dim == \"multivariate\":\n",
    "        for i in range(1, df_dataset.shape[1]-1):\n",
    "            fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, i], name=f\"channel-{i}\"), 1, 1)\n",
    "    else:\n",
    "        fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, 1], name=\"timeseries\"), 1, 1)\n",
    "    fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset[\"is_anomaly\"], name=\"label\"), 2, 1)\n",
    "    \n",
    "    for item in algorithms:\n",
    "        algo, optim_params = item\n",
    "        fig.add_trace(go.Scatter(x=df_scores.index, y=df_scores[item], name=f\"{algo}={auroc[item]:.4f} ({optim_params})\"), 2, 1)\n",
    "    fig.update_xaxes(matches=\"x\")\n",
    "    fig.update_layout(\n",
    "        title=f\"Results of {','.join(np.unique([a for a, _ in algorithms]))} on {dataset_name}\",\n",
    "        height=400\n",
    "    )\n",
    "    return py.iplot(fig)\n",
    "\n",
    "def plot_scores_plt(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs):\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    # Create plot\n",
    "    fig, axs = plt.subplots(2, 1, sharex=True, figsize=(20, 8))\n",
    "    if dataset_dim == \"multivariate\":\n",
    "        for i in range(1, df_dataset.shape[1]-1):\n",
    "            axs[0].plot(df_dataset.index, df_dataset.iloc[:, i], label=f\"channel-{i}\")\n",
    "    else:\n",
    "        axs[0].plot(df_dataset.index, df_dataset.iloc[:, 1], label=f\"timeseries\")\n",
    "    axs[1].plot(df_dataset.index, df_dataset[\"is_anomaly\"], label=\"label\")\n",
    "    \n",
    "    for item in algorithms:\n",
    "        algo, optim_params = item\n",
    "        axs[1].plot(df_scores.index, df_scores[item], label=f\"{algo}={auroc[item]:.4f} ({optim_params})\")\n",
    "    axs[0].legend()\n",
    "    axs[1].legend()\n",
    "    fig.suptitle(f\"Results of {','.join(np.unique([a for a, _ in algorithms]))} on {dataset_name}\")\n",
    "    fig.tight_layout()\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">PR_AUC</th>\n",
       "      <th colspan=\"3\" halign=\"left\">ROC_AUC</th>\n",
       "      <th>train_main_time</th>\n",
       "      <th>execute_main_time</th>\n",
       "      <th>repetition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>algorithm</th>\n",
       "      <th>optim_param_name</th>\n",
       "      <th>optim_param_value</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">FFT</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">max_sign_change_distance</th>\n",
       "      <th>5.0000</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.404383</td>\n",
       "      <td>0.476016</td>\n",
       "      <td>0.005101</td>\n",
       "      <td>0.642634</td>\n",
       "      <td>0.581943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.148283</td>\n",
       "      <td>168</td>\n",
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       "    <tr>\n",
       "      <th>20.0000</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.405193</td>\n",
       "      <td>0.500050</td>\n",
       "      <td>0.010909</td>\n",
       "      <td>0.639064</td>\n",
       "      <td>0.590061</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.867943</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0000</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.401553</td>\n",
       "      <td>0.500050</td>\n",
       "      <td>0.010909</td>\n",
       "      <td>0.636789</td>\n",
       "      <td>0.566659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.797005</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0000</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.404115</td>\n",
       "      <td>0.500050</td>\n",
       "      <td>0.010909</td>\n",
       "      <td>0.636391</td>\n",
       "      <td>0.585594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.407711</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">local_outlier_threshold</th>\n",
       "      <th>0.7800</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.428873</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.014141</td>\n",
       "      <td>0.639053</td>\n",
       "      <td>0.575039</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.780453</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6000</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.401553</td>\n",
       "      <td>0.500050</td>\n",
       "      <td>0.010909</td>\n",
       "      <td>0.636789</td>\n",
       "      <td>0.566659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.935648</td>\n",
       "      <td>168</td>\n",
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       "    <tr>\n",
       "      <th>0.4200</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.389398</td>\n",
       "      <td>0.393046</td>\n",
       "      <td>0.008434</td>\n",
       "      <td>0.636015</td>\n",
       "      <td>0.579356</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.162488</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">fft_parameters</th>\n",
       "      <th>1.0000</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.401553</td>\n",
       "      <td>0.500050</td>\n",
       "      <td>0.010909</td>\n",
       "      <td>0.636789</td>\n",
       "      <td>0.566659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.533686</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0000</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.401553</td>\n",
       "      <td>0.500050</td>\n",
       "      <td>0.010909</td>\n",
       "      <td>0.636789</td>\n",
       "      <td>0.566659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.839319</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0000</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.401553</td>\n",
       "      <td>0.500050</td>\n",
       "      <td>0.010909</td>\n",
       "      <td>0.636789</td>\n",
       "      <td>0.566659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.147953</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0000</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.401553</td>\n",
       "      <td>0.500050</td>\n",
       "      <td>0.010909</td>\n",
       "      <td>0.636789</td>\n",
       "      <td>0.566659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.835031</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0000</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.401553</td>\n",
       "      <td>0.500050</td>\n",
       "      <td>0.010909</td>\n",
       "      <td>0.636789</td>\n",
       "      <td>0.566659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.178521</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">Donut</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">window_size</th>\n",
       "      <th>1.0000</th>\n",
       "      <td>0.000191</td>\n",
       "      <td>0.457369</td>\n",
       "      <td>0.440656</td>\n",
       "      <td>0.167551</td>\n",
       "      <td>0.889480</td>\n",
       "      <td>0.971284</td>\n",
       "      <td>261.667392</td>\n",
       "      <td>88.006721</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5000</th>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.422099</td>\n",
       "      <td>0.326136</td>\n",
       "      <td>0.129022</td>\n",
       "      <td>0.865502</td>\n",
       "      <td>0.966446</td>\n",
       "      <td>287.565262</td>\n",
       "      <td>111.784334</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5000</th>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.468037</td>\n",
       "      <td>0.501775</td>\n",
       "      <td>0.115970</td>\n",
       "      <td>0.865069</td>\n",
       "      <td>0.939621</td>\n",
       "      <td>236.515266</td>\n",
       "      <td>61.451180</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0000</th>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.398320</td>\n",
       "      <td>0.269384</td>\n",
       "      <td>0.088718</td>\n",
       "      <td>0.861572</td>\n",
       "      <td>0.955066</td>\n",
       "      <td>304.455652</td>\n",
       "      <td>140.248628</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">linear_hidden_size</th>\n",
       "      <th>130.0000</th>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.317636</td>\n",
       "      <td>0.296335</td>\n",
       "      <td>0.060342</td>\n",
       "      <td>0.896637</td>\n",
       "      <td>0.970845</td>\n",
       "      <td>335.481505</td>\n",
       "      <td>140.662351</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70.0000</th>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.325304</td>\n",
       "      <td>0.309167</td>\n",
       "      <td>0.012014</td>\n",
       "      <td>0.882940</td>\n",
       "      <td>0.968023</td>\n",
       "      <td>238.004034</td>\n",
       "      <td>101.803984</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.0000</th>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.327029</td>\n",
       "      <td>0.297545</td>\n",
       "      <td>0.044967</td>\n",
       "      <td>0.880596</td>\n",
       "      <td>0.965979</td>\n",
       "      <td>296.645641</td>\n",
       "      <td>124.091863</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">latent_size</th>\n",
       "      <th>3.0000</th>\n",
       "      <td>0.000339</td>\n",
       "      <td>0.321554</td>\n",
       "      <td>0.304011</td>\n",
       "      <td>0.018791</td>\n",
       "      <td>0.887716</td>\n",
       "      <td>0.971029</td>\n",
       "      <td>265.684349</td>\n",
       "      <td>99.918567</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0000</th>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.326849</td>\n",
       "      <td>0.298401</td>\n",
       "      <td>0.003028</td>\n",
       "      <td>0.887621</td>\n",
       "      <td>0.970871</td>\n",
       "      <td>282.107605</td>\n",
       "      <td>110.539883</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0000</th>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.327026</td>\n",
       "      <td>0.297747</td>\n",
       "      <td>0.044967</td>\n",
       "      <td>0.880316</td>\n",
       "      <td>0.967740</td>\n",
       "      <td>275.504970</td>\n",
       "      <td>104.584672</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">DeepAnT</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">window_size</th>\n",
       "      <th>1.0000</th>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.591800</td>\n",
       "      <td>0.713085</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.861454</td>\n",
       "      <td>0.993757</td>\n",
       "      <td>4268.741428</td>\n",
       "      <td>10.901872</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5000</th>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.655989</td>\n",
       "      <td>0.896235</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.855418</td>\n",
       "      <td>0.997640</td>\n",
       "      <td>4280.982872</td>\n",
       "      <td>9.757153</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0000</th>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.540441</td>\n",
       "      <td>0.579040</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.854839</td>\n",
       "      <td>0.986439</td>\n",
       "      <td>4418.571583</td>\n",
       "      <td>13.035728</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5000</th>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.529274</td>\n",
       "      <td>0.514126</td>\n",
       "      <td>0.000376</td>\n",
       "      <td>0.841131</td>\n",
       "      <td>0.990508</td>\n",
       "      <td>4596.981345</td>\n",
       "      <td>12.260055</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">DWT-MLEAD</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">quantile_epsilon</th>\n",
       "      <th>0.1000</th>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.397487</td>\n",
       "      <td>0.325057</td>\n",
       "      <td>0.125859</td>\n",
       "      <td>0.896714</td>\n",
       "      <td>0.970002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.014893</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0100</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.344185</td>\n",
       "      <td>0.224233</td>\n",
       "      <td>0.181281</td>\n",
       "      <td>0.841128</td>\n",
       "      <td>0.952890</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.205069</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0010</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.224638</td>\n",
       "      <td>0.071084</td>\n",
       "      <td>0.286186</td>\n",
       "      <td>0.717423</td>\n",
       "      <td>0.712793</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.023141</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"21\" valign=\"top\">DBStream</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">window_size</th>\n",
       "      <th>1.0000</th>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.553839</td>\n",
       "      <td>0.768145</td>\n",
       "      <td>0.233232</td>\n",
       "      <td>0.836469</td>\n",
       "      <td>0.946486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.340152</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5000</th>\n",
       "      <td>0.000784</td>\n",
       "      <td>0.525955</td>\n",
       "      <td>0.490232</td>\n",
       "      <td>0.362222</td>\n",
       "      <td>0.828312</td>\n",
       "      <td>0.937338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.074414</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5000</th>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.515533</td>\n",
       "      <td>0.622105</td>\n",
       "      <td>0.201627</td>\n",
       "      <td>0.802834</td>\n",
       "      <td>0.967927</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108.110891</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0000</th>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.484655</td>\n",
       "      <td>0.419526</td>\n",
       "      <td>0.161689</td>\n",
       "      <td>0.785506</td>\n",
       "      <td>0.932110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.618125</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">shared_density</th>\n",
       "      <th>0.0000</th>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.563529</td>\n",
       "      <td>0.725519</td>\n",
       "      <td>0.226153</td>\n",
       "      <td>0.834281</td>\n",
       "      <td>0.945367</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.935346</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0000</th>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.553451</td>\n",
       "      <td>0.689847</td>\n",
       "      <td>0.226153</td>\n",
       "      <td>0.827472</td>\n",
       "      <td>0.943877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.294384</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">radius</th>\n",
       "      <th>0.1300</th>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.651889</td>\n",
       "      <td>0.851538</td>\n",
       "      <td>0.342172</td>\n",
       "      <td>0.846145</td>\n",
       "      <td>0.956418</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.248086</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1000</th>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.584877</td>\n",
       "      <td>0.771458</td>\n",
       "      <td>0.310960</td>\n",
       "      <td>0.844604</td>\n",
       "      <td>0.951132</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.227686</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0700</th>\n",
       "      <td>0.001626</td>\n",
       "      <td>0.495407</td>\n",
       "      <td>0.441237</td>\n",
       "      <td>0.063352</td>\n",
       "      <td>0.810140</td>\n",
       "      <td>0.922847</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.748553</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">min_weight</th>\n",
       "      <th>0.0000</th>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.585341</td>\n",
       "      <td>0.771458</td>\n",
       "      <td>0.310960</td>\n",
       "      <td>0.847200</td>\n",
       "      <td>0.951132</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.046572</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2000</th>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.560990</td>\n",
       "      <td>0.568899</td>\n",
       "      <td>0.310960</td>\n",
       "      <td>0.823789</td>\n",
       "      <td>0.931622</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.098913</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5000</th>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.548929</td>\n",
       "      <td>0.558690</td>\n",
       "      <td>0.310960</td>\n",
       "      <td>0.809120</td>\n",
       "      <td>0.876899</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.297332</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">lambda</th>\n",
       "      <th>0.0010</th>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.588656</td>\n",
       "      <td>0.777893</td>\n",
       "      <td>0.310960</td>\n",
       "      <td>0.846978</td>\n",
       "      <td>0.951319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.523327</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0001</th>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.563101</td>\n",
       "      <td>0.685540</td>\n",
       "      <td>0.230657</td>\n",
       "      <td>0.832205</td>\n",
       "      <td>0.940568</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.755518</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0100</th>\n",
       "      <td>0.005281</td>\n",
       "      <td>0.564632</td>\n",
       "      <td>0.666820</td>\n",
       "      <td>0.233424</td>\n",
       "      <td>0.822484</td>\n",
       "      <td>0.935941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.331981</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1000</th>\n",
       "      <td>0.005281</td>\n",
       "      <td>0.528014</td>\n",
       "      <td>0.508826</td>\n",
       "      <td>0.149253</td>\n",
       "      <td>0.748329</td>\n",
       "      <td>0.759798</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.810063</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">alpha</th>\n",
       "      <th>0.5000</th>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.574242</td>\n",
       "      <td>0.749917</td>\n",
       "      <td>0.310960</td>\n",
       "      <td>0.839715</td>\n",
       "      <td>0.948042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.940806</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0100</th>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.560262</td>\n",
       "      <td>0.707740</td>\n",
       "      <td>0.226153</td>\n",
       "      <td>0.832397</td>\n",
       "      <td>0.945186</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.047287</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2000</th>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.556440</td>\n",
       "      <td>0.689960</td>\n",
       "      <td>0.226153</td>\n",
       "      <td>0.830784</td>\n",
       "      <td>0.945005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.780118</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1000</th>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.556542</td>\n",
       "      <td>0.689960</td>\n",
       "      <td>0.226153</td>\n",
       "      <td>0.825852</td>\n",
       "      <td>0.945005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.757660</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        PR_AUC            \\\n",
       "                                                           min      mean   \n",
       "algorithm optim_param_name         optim_param_value                       \n",
       "FFT       max_sign_change_distance 5.0000             0.000050  0.404383   \n",
       "                                   20.0000            0.000050  0.405193   \n",
       "                                   10.0000            0.000050  0.401553   \n",
       "                                   30.0000            0.000050  0.404115   \n",
       "          local_outlier_threshold  0.7800             0.000050  0.428873   \n",
       "                                   0.6000             0.000050  0.401553   \n",
       "                                   0.4200             0.000050  0.389398   \n",
       "          fft_parameters           1.0000             0.000050  0.401553   \n",
       "                                   2.0000             0.000050  0.401553   \n",
       "                                   3.0000             0.000050  0.401553   \n",
       "                                   5.0000             0.000050  0.401553   \n",
       "                                   7.0000             0.000050  0.401553   \n",
       "Donut     window_size              1.0000             0.000191  0.457369   \n",
       "                                   1.5000             0.000118  0.422099   \n",
       "                                   0.5000             0.000214  0.468037   \n",
       "                                   2.0000             0.000082  0.398320   \n",
       "          linear_hidden_size       130.0000           0.000157  0.317636   \n",
       "                                   70.0000            0.000179  0.325304   \n",
       "                                   100.0000           0.000107  0.327029   \n",
       "          latent_size              3.0000             0.000339  0.321554   \n",
       "                                   6.0000             0.000171  0.326849   \n",
       "                                   5.0000             0.000107  0.327026   \n",
       "DeepAnT   window_size              1.0000             0.000066  0.591800   \n",
       "                                   0.5000             0.000070  0.655989   \n",
       "                                   2.0000             0.000080  0.540441   \n",
       "                                   1.5000             0.000060  0.529274   \n",
       "DWT-MLEAD quantile_epsilon         0.1000             0.000120  0.397487   \n",
       "                                   0.0100             0.000050  0.344185   \n",
       "                                   0.0010             0.000050  0.224638   \n",
       "DBStream  window_size              1.0000             0.000111  0.553839   \n",
       "                                   0.5000             0.000784  0.525955   \n",
       "                                   1.5000             0.000095  0.515533   \n",
       "                                   2.0000             0.000095  0.484655   \n",
       "          shared_density           0.0000             0.005000  0.563529   \n",
       "                                   1.0000             0.005000  0.553451   \n",
       "          radius                   0.1300             0.005000  0.651889   \n",
       "                                   0.1000             0.005000  0.584877   \n",
       "                                   0.0700             0.001626  0.495407   \n",
       "          min_weight               0.0000             0.005000  0.585341   \n",
       "                                   0.2000             0.005000  0.560990   \n",
       "                                   0.5000             0.005000  0.548929   \n",
       "                                   1.0000                  NaN       NaN   \n",
       "          lambda                   0.0010             0.005000  0.588656   \n",
       "                                   0.0001             0.005000  0.563101   \n",
       "                                   0.0100             0.005281  0.564632   \n",
       "                                   0.1000             0.005281  0.528014   \n",
       "          alpha                    0.5000             0.005000  0.574242   \n",
       "                                   0.0100             0.005000  0.560262   \n",
       "                                   0.2000             0.005000  0.556440   \n",
       "                                   0.1000             0.005000  0.556542   \n",
       "\n",
       "                                                                 ROC_AUC  \\\n",
       "                                                        median       min   \n",
       "algorithm optim_param_name         optim_param_value                       \n",
       "FFT       max_sign_change_distance 5.0000             0.476016  0.005101   \n",
       "                                   20.0000            0.500050  0.010909   \n",
       "                                   10.0000            0.500050  0.010909   \n",
       "                                   30.0000            0.500050  0.010909   \n",
       "          local_outlier_threshold  0.7800             0.505000  0.014141   \n",
       "                                   0.6000             0.500050  0.010909   \n",
       "                                   0.4200             0.393046  0.008434   \n",
       "          fft_parameters           1.0000             0.500050  0.010909   \n",
       "                                   2.0000             0.500050  0.010909   \n",
       "                                   3.0000             0.500050  0.010909   \n",
       "                                   5.0000             0.500050  0.010909   \n",
       "                                   7.0000             0.500050  0.010909   \n",
       "Donut     window_size              1.0000             0.440656  0.167551   \n",
       "                                   1.5000             0.326136  0.129022   \n",
       "                                   0.5000             0.501775  0.115970   \n",
       "                                   2.0000             0.269384  0.088718   \n",
       "          linear_hidden_size       130.0000           0.296335  0.060342   \n",
       "                                   70.0000            0.309167  0.012014   \n",
       "                                   100.0000           0.297545  0.044967   \n",
       "          latent_size              3.0000             0.304011  0.018791   \n",
       "                                   6.0000             0.298401  0.003028   \n",
       "                                   5.0000             0.297747  0.044967   \n",
       "DeepAnT   window_size              1.0000             0.713085  0.000242   \n",
       "                                   0.5000             0.896235  0.000019   \n",
       "                                   2.0000             0.579040  0.000020   \n",
       "                                   1.5000             0.514126  0.000376   \n",
       "DWT-MLEAD quantile_epsilon         0.1000             0.325057  0.125859   \n",
       "                                   0.0100             0.224233  0.181281   \n",
       "                                   0.0010             0.071084  0.286186   \n",
       "DBStream  window_size              1.0000             0.768145  0.233232   \n",
       "                                   0.5000             0.490232  0.362222   \n",
       "                                   1.5000             0.622105  0.201627   \n",
       "                                   2.0000             0.419526  0.161689   \n",
       "          shared_density           0.0000             0.725519  0.226153   \n",
       "                                   1.0000             0.689847  0.226153   \n",
       "          radius                   0.1300             0.851538  0.342172   \n",
       "                                   0.1000             0.771458  0.310960   \n",
       "                                   0.0700             0.441237  0.063352   \n",
       "          min_weight               0.0000             0.771458  0.310960   \n",
       "                                   0.2000             0.568899  0.310960   \n",
       "                                   0.5000             0.558690  0.310960   \n",
       "                                   1.0000                  NaN       NaN   \n",
       "          lambda                   0.0010             0.777893  0.310960   \n",
       "                                   0.0001             0.685540  0.230657   \n",
       "                                   0.0100             0.666820  0.233424   \n",
       "                                   0.1000             0.508826  0.149253   \n",
       "          alpha                    0.5000             0.749917  0.310960   \n",
       "                                   0.0100             0.707740  0.226153   \n",
       "                                   0.2000             0.689960  0.226153   \n",
       "                                   0.1000             0.689960  0.226153   \n",
       "\n",
       "                                                                          \\\n",
       "                                                          mean    median   \n",
       "algorithm optim_param_name         optim_param_value                       \n",
       "FFT       max_sign_change_distance 5.0000             0.642634  0.581943   \n",
       "                                   20.0000            0.639064  0.590061   \n",
       "                                   10.0000            0.636789  0.566659   \n",
       "                                   30.0000            0.636391  0.585594   \n",
       "          local_outlier_threshold  0.7800             0.639053  0.575039   \n",
       "                                   0.6000             0.636789  0.566659   \n",
       "                                   0.4200             0.636015  0.579356   \n",
       "          fft_parameters           1.0000             0.636789  0.566659   \n",
       "                                   2.0000             0.636789  0.566659   \n",
       "                                   3.0000             0.636789  0.566659   \n",
       "                                   5.0000             0.636789  0.566659   \n",
       "                                   7.0000             0.636789  0.566659   \n",
       "Donut     window_size              1.0000             0.889480  0.971284   \n",
       "                                   1.5000             0.865502  0.966446   \n",
       "                                   0.5000             0.865069  0.939621   \n",
       "                                   2.0000             0.861572  0.955066   \n",
       "          linear_hidden_size       130.0000           0.896637  0.970845   \n",
       "                                   70.0000            0.882940  0.968023   \n",
       "                                   100.0000           0.880596  0.965979   \n",
       "          latent_size              3.0000             0.887716  0.971029   \n",
       "                                   6.0000             0.887621  0.970871   \n",
       "                                   5.0000             0.880316  0.967740   \n",
       "DeepAnT   window_size              1.0000             0.861454  0.993757   \n",
       "                                   0.5000             0.855418  0.997640   \n",
       "                                   2.0000             0.854839  0.986439   \n",
       "                                   1.5000             0.841131  0.990508   \n",
       "DWT-MLEAD quantile_epsilon         0.1000             0.896714  0.970002   \n",
       "                                   0.0100             0.841128  0.952890   \n",
       "                                   0.0010             0.717423  0.712793   \n",
       "DBStream  window_size              1.0000             0.836469  0.946486   \n",
       "                                   0.5000             0.828312  0.937338   \n",
       "                                   1.5000             0.802834  0.967927   \n",
       "                                   2.0000             0.785506  0.932110   \n",
       "          shared_density           0.0000             0.834281  0.945367   \n",
       "                                   1.0000             0.827472  0.943877   \n",
       "          radius                   0.1300             0.846145  0.956418   \n",
       "                                   0.1000             0.844604  0.951132   \n",
       "                                   0.0700             0.810140  0.922847   \n",
       "          min_weight               0.0000             0.847200  0.951132   \n",
       "                                   0.2000             0.823789  0.931622   \n",
       "                                   0.5000             0.809120  0.876899   \n",
       "                                   1.0000                  NaN       NaN   \n",
       "          lambda                   0.0010             0.846978  0.951319   \n",
       "                                   0.0001             0.832205  0.940568   \n",
       "                                   0.0100             0.822484  0.935941   \n",
       "                                   0.1000             0.748329  0.759798   \n",
       "          alpha                    0.5000             0.839715  0.948042   \n",
       "                                   0.0100             0.832397  0.945186   \n",
       "                                   0.2000             0.830784  0.945005   \n",
       "                                   0.1000             0.825852  0.945005   \n",
       "\n",
       "                                                     train_main_time  \\\n",
       "                                                                mean   \n",
       "algorithm optim_param_name         optim_param_value                   \n",
       "FFT       max_sign_change_distance 5.0000                        NaN   \n",
       "                                   20.0000                       NaN   \n",
       "                                   10.0000                       NaN   \n",
       "                                   30.0000                       NaN   \n",
       "          local_outlier_threshold  0.7800                        NaN   \n",
       "                                   0.6000                        NaN   \n",
       "                                   0.4200                        NaN   \n",
       "          fft_parameters           1.0000                        NaN   \n",
       "                                   2.0000                        NaN   \n",
       "                                   3.0000                        NaN   \n",
       "                                   5.0000                        NaN   \n",
       "                                   7.0000                        NaN   \n",
       "Donut     window_size              1.0000                 261.667392   \n",
       "                                   1.5000                 287.565262   \n",
       "                                   0.5000                 236.515266   \n",
       "                                   2.0000                 304.455652   \n",
       "          linear_hidden_size       130.0000               335.481505   \n",
       "                                   70.0000                238.004034   \n",
       "                                   100.0000               296.645641   \n",
       "          latent_size              3.0000                 265.684349   \n",
       "                                   6.0000                 282.107605   \n",
       "                                   5.0000                 275.504970   \n",
       "DeepAnT   window_size              1.0000                4268.741428   \n",
       "                                   0.5000                4280.982872   \n",
       "                                   2.0000                4418.571583   \n",
       "                                   1.5000                4596.981345   \n",
       "DWT-MLEAD quantile_epsilon         0.1000                        NaN   \n",
       "                                   0.0100                        NaN   \n",
       "                                   0.0010                        NaN   \n",
       "DBStream  window_size              1.0000                        NaN   \n",
       "                                   0.5000                        NaN   \n",
       "                                   1.5000                        NaN   \n",
       "                                   2.0000                        NaN   \n",
       "          shared_density           0.0000                        NaN   \n",
       "                                   1.0000                        NaN   \n",
       "          radius                   0.1300                        NaN   \n",
       "                                   0.1000                        NaN   \n",
       "                                   0.0700                        NaN   \n",
       "          min_weight               0.0000                        NaN   \n",
       "                                   0.2000                        NaN   \n",
       "                                   0.5000                        NaN   \n",
       "                                   1.0000                        NaN   \n",
       "          lambda                   0.0010                        NaN   \n",
       "                                   0.0001                        NaN   \n",
       "                                   0.0100                        NaN   \n",
       "                                   0.1000                        NaN   \n",
       "          alpha                    0.5000                        NaN   \n",
       "                                   0.0100                        NaN   \n",
       "                                   0.2000                        NaN   \n",
       "                                   0.1000                        NaN   \n",
       "\n",
       "                                                     execute_main_time  \\\n",
       "                                                                  mean   \n",
       "algorithm optim_param_name         optim_param_value                     \n",
       "FFT       max_sign_change_distance 5.0000                     5.148283   \n",
       "                                   20.0000                    5.867943   \n",
       "                                   10.0000                    5.797005   \n",
       "                                   30.0000                    5.407711   \n",
       "          local_outlier_threshold  0.7800                     5.780453   \n",
       "                                   0.6000                     5.935648   \n",
       "                                   0.4200                     6.162488   \n",
       "          fft_parameters           1.0000                     5.533686   \n",
       "                                   2.0000                     5.839319   \n",
       "                                   3.0000                     6.147953   \n",
       "                                   5.0000                     5.835031   \n",
       "                                   7.0000                     6.178521   \n",
       "Donut     window_size              1.0000                    88.006721   \n",
       "                                   1.5000                   111.784334   \n",
       "                                   0.5000                    61.451180   \n",
       "                                   2.0000                   140.248628   \n",
       "          linear_hidden_size       130.0000                 140.662351   \n",
       "                                   70.0000                  101.803984   \n",
       "                                   100.0000                 124.091863   \n",
       "          latent_size              3.0000                    99.918567   \n",
       "                                   6.0000                   110.539883   \n",
       "                                   5.0000                   104.584672   \n",
       "DeepAnT   window_size              1.0000                    10.901872   \n",
       "                                   0.5000                     9.757153   \n",
       "                                   2.0000                    13.035728   \n",
       "                                   1.5000                    12.260055   \n",
       "DWT-MLEAD quantile_epsilon         0.1000                     8.014893   \n",
       "                                   0.0100                     8.205069   \n",
       "                                   0.0010                     8.023141   \n",
       "DBStream  window_size              1.0000                    63.340152   \n",
       "                                   0.5000                    33.074414   \n",
       "                                   1.5000                   108.110891   \n",
       "                                   2.0000                   180.618125   \n",
       "          shared_density           0.0000                    27.935346   \n",
       "                                   1.0000                    28.294384   \n",
       "          radius                   0.1300                    20.248086   \n",
       "                                   0.1000                    28.227686   \n",
       "                                   0.0700                    31.748553   \n",
       "          min_weight               0.0000                    24.046572   \n",
       "                                   0.2000                    11.098913   \n",
       "                                   0.5000                     9.297332   \n",
       "                                   1.0000                          NaN   \n",
       "          lambda                   0.0010                    24.523327   \n",
       "                                   0.0001                    27.755518   \n",
       "                                   0.0100                    28.331981   \n",
       "                                   0.1000                    26.810063   \n",
       "          alpha                    0.5000                    25.940806   \n",
       "                                   0.0100                    31.047287   \n",
       "                                   0.2000                    28.780118   \n",
       "                                   0.1000                    29.757660   \n",
       "\n",
       "                                                     repetition  \n",
       "                                                          count  \n",
       "algorithm optim_param_name         optim_param_value             \n",
       "FFT       max_sign_change_distance 5.0000                   168  \n",
       "                                   20.0000                  168  \n",
       "                                   10.0000                  168  \n",
       "                                   30.0000                  168  \n",
       "          local_outlier_threshold  0.7800                   168  \n",
       "                                   0.6000                   168  \n",
       "                                   0.4200                   168  \n",
       "          fft_parameters           1.0000                   168  \n",
       "                                   2.0000                   168  \n",
       "                                   3.0000                   168  \n",
       "                                   5.0000                   168  \n",
       "                                   7.0000                   168  \n",
       "Donut     window_size              1.0000                   168  \n",
       "                                   1.5000                   168  \n",
       "                                   0.5000                   168  \n",
       "                                   2.0000                   168  \n",
       "          linear_hidden_size       130.0000                 168  \n",
       "                                   70.0000                  168  \n",
       "                                   100.0000                 168  \n",
       "          latent_size              3.0000                   168  \n",
       "                                   6.0000                   168  \n",
       "                                   5.0000                   168  \n",
       "DeepAnT   window_size              1.0000                   193  \n",
       "                                   0.5000                   193  \n",
       "                                   2.0000                   193  \n",
       "                                   1.5000                   193  \n",
       "DWT-MLEAD quantile_epsilon         0.1000                   168  \n",
       "                                   0.0100                   168  \n",
       "                                   0.0010                   168  \n",
       "DBStream  window_size              1.0000                   193  \n",
       "                                   0.5000                   193  \n",
       "                                   1.5000                   193  \n",
       "                                   2.0000                   193  \n",
       "          shared_density           0.0000                   193  \n",
       "                                   1.0000                   193  \n",
       "          radius                   0.1300                   193  \n",
       "                                   0.1000                   193  \n",
       "                                   0.0700                   193  \n",
       "          min_weight               0.0000                   193  \n",
       "                                   0.2000                   193  \n",
       "                                   0.5000                   193  \n",
       "                                   1.0000                   193  \n",
       "          lambda                   0.0010                   193  \n",
       "                                   0.0001                   193  \n",
       "                                   0.0100                   193  \n",
       "                                   0.1000                   193  \n",
       "          alpha                    0.5000                   193  \n",
       "                                   0.0100                   193  \n",
       "                                   0.2000                   193  \n",
       "                                   0.1000                   193  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_by = (\"ROC_AUC\", \"mean\")\n",
    "metric_agg_type = [\"min\", \"mean\", \"median\"]\n",
    "time_agg_type = \"mean\"\n",
    "aggs = {\n",
    "    \"PR_AUC\": metric_agg_type,\n",
    "    \"ROC_AUC\": metric_agg_type,\n",
    "    \"train_main_time\": time_agg_type,\n",
    "    \"execute_main_time\": time_agg_type,\n",
    "    \"repetition\": \"count\"\n",
    "}\n",
    "\n",
    "df_tmp = df.reset_index()\n",
    "df_tmp = df_tmp.groupby(by=[\"algorithm\", \"optim_param_name\", \"optim_param_value\"]).agg(aggs)\n",
    "df_tmp = df_tmp.reset_index()\n",
    "df_tmp = df_tmp.sort_values(by=[\"algorithm\", \"optim_param_name\", sort_by], ascending=False)\n",
    "df_tmp = df_tmp.set_index([\"algorithm\", \"optim_param_name\", \"optim_param_value\"])\n",
    "\n",
    "with pd.option_context(\"display.max_rows\", None, \"display.max_columns\", None):\n",
    "    display(df_tmp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selected parameters\n",
    "\n",
    "- FFT: `max_sign_change_distance=20,local_outlier_threshold=0.78,fft_parameters=3` (no real improvement, overall bad)\n",
    "- Donut: `window_size=\"1.0 dataset period size\",linear_hidden_size=130,latent_size=5` (no real improvement, overall good)\n",
    "- DeepAnT: `window_size=\"0.5 dataset period size\"` (0.5 slightly worse than 1.0 for ROC_AUC, but way better for PR_AUC, therefore: 0.5)\n",
    "- DWT-MLEAD: `quantile_epsilon=0.1` (easy: larger is better)\n",
    "- DBStream: `window_size=\"1.0 dataset period size\",shared_density=True,radius=1.3,min_weight=0,lambda=0.001,alpha=0.5` (window_size: either 0.5 or 1.0... depending on the dataset; radius: higher is better; min_weight: lower is better)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores([\n",
    "    (\"DeepAnT\", \"window_size\", 0.5),\n",
    "    (\"DeepAnT\", \"window_size\", 1.0)\n",
    "], \"ecg-combined-diff-2\", use_plotly=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores([\n",
    "    (\"DWT-MLEAD\", \"quantile_epsilon\", 0.1),\n",
    "    (\"DWT-MLEAD\", \"quantile_epsilon\", 0.001)\n",
    "], \"sinus-type-frequency\", use_plotly=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores([\n",
    "    (\"DBStream\", \"window_size\", 1),\n",
    "    (\"DBStream\", \"window_size\", 0.5)\n",
    "], \"poly-diff-count-5\", use_plotly=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "timeeval38",
   "language": "python",
   "name": "timeeval38"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
